• 土地覆被与土地利用研究 •

### 干旱荒漠区植被覆盖变化的遥感监测分析

1. 1. 石河子大学生命科学学院,石河子 832000;
2. 中国科学院地理科学与资源研究所,北京 100101;
3. 中国科学院新疆生态与地理研究所,乌鲁木齐 830011
• 收稿日期:2011-02-22 修回日期:2011-05-30 出版日期:2011-06-25 发布日期:2011-06-15
• 通讯作者: 刘 彤(1968-),男,博士,教授。主要从事植被生态和进化生物学研究。E-mail:liutong1968@yahoo.com.cn E-mail:liutong1968@yahoo.com.cn
• 作者简介:崔耀平(1984-),男,博士研究生,主要从事遥感应用与环境信息模拟研究。E-mail:cuiyp@lreis.ac.cn
• 基金资助:

国家十一五科技支撑计划重大项目(2007BAC17B03)资助。

### Using Multi-spectral Remote Sensing Data to Extract and Analyze the Vegetation Change of the Western Gurbantunggut Desert

CUI Yaoping1,2, LIU Tong1, ZHAO Zhiping2, LI Jia3

1. 1. College of Life Sciences, Shihezi University, Shihezi 832000, China;
2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
3. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
• Received:2011-02-22 Revised:2011-05-30 Online:2011-06-25 Published:2011-06-15

Abstract: Acquiring vegetation information is a key step in monitoring and evaluating arid land cover, even though it is difficult to extract desert vegetation information due to the confounding effort of soil or sand. There are many limiting factors for estimating vegetation growth in arid landscapes. Nevertheless, the growth of perennial vegetation reflects comprehensive vegetation conditions and can represent vegetation cover during a certain period. So in this study, we analyzed three years of Landsat images (1989, 2000 and 2007) that covered a typical portion of the Gurbantunggut Desert to estimate the vegetation change that has occurred. After comparing different methods, we finally chose the U-min method (one of the spectral mixture analysis methods) as the best way to determine the fraction of vegetation information of Landsat images. The RGB composition method was used to monitor the changes in vegetation abundance. In the end, we also comprehensively estimated vegetation changes using annual average time-series precipitation data and other's NDVI research results. The main results showed that: (1) a significant linear relationship exists between vegetation cover and vegetation fraction, with a correlation coefficient of 0.858. So the vegetation cover percent can be expressed by vegetation fraction extracted from remote sense images. (2) The area with improved vegetation cover accounted for 41.47% of the whole study area, while these patches with degraded vegetation cover accounting for 16.51%. However, it should be noted that these degraded vegetation pixels were distributed more sporadically. (3) During the study period, improved vegetation regions were mainly located in the semi-fixed dunes. At the same time, the analysis results also showed that the vegetation was distributed extensively in the arid land, regions including vegetation grown occupied almost 90% of the whole study area, and the number of improved vegetation pixels was greater than the number of degraded vegetation pixels during the period of 20 years.